US 2025/0053934 A1 · DAY-AHEAD COORDINATED OPTIMIZATION SCHEDULING METHOD FOR TRUNK MOBILE CHARGING STATION

Abstract

The present disclosure relates to the technical field of optimization of charging facilities, and in particular, to a day-ahead coordinated optimization scheduling method for a truck mobile charging station. The method includes: constructing an optimization scheduling model framework, wherein the optimization scheduling model framework includes: an EV charging demand generation model and a TMCS spatial-temporal model, the EV model is used to determine the position and time of the TMCS charging demand, and the TMCS scheduling model is used to describe the spatial-temporal dynamic characteristics of TMCS operation and complete the coordinated optimization scheduling of TMCS between EV charging service and energy arbitrage; capturing a charging decision process of heterogeneous EV users by adopting MCS and a multinomial Logit model; establishing an extended graph model to describe the spatial-temporal dynamic characteristics of TMCS; and then expressing the coordinated scheduling model as a mixed integer linear programming model.

A day-ahead coordinated optimization scheduling method for a truck mobile charging station (TMCS), comprising:

S1:
constructing an electric vehicle (EV) model, and generating spatial-temporal distribution of EV charging demands for a fixed charging station (FCS) and the TMCS by using the EV model according to traffic flow prediction data and configuration information of the FCS and the TMCS;
and
S2:
constructing a TMCS scheduling model, wherein the TMCS scheduling model describes spatial-temporal dynamic characteristics of TMCS operation and completes coordinated optimization scheduling of TMCS between EV charging service and energy arbitrage, and establishing a day-ahead optimization scheduling method for TMCS to maximize the profitability of a charging facility operator (CFO);
in the step S1, the market share and energy consumption characteristics of different types of EVs are obtained according to market sales data, wherein the energy consumption characteristics of EVs comprise battery capacity and probability distribution of energy consumption per unit mileage;
a departure time and a probability density function of initial values of state of charge (SOC) of the EVs under unified confidence are obtained by fitting EV charging;
start and end points of the EV are generated by an origin-destination (OD) analysis method, and a travel path is obtained based on Monte Carlo simulation and a Floyd algorithm;
EVi needs to be charged on the way, and potential charging selection solutions are generated according to road and travel limits, SOC data and charging station positions, wherein a set of the charging selection solutions is denoted as Si, Si comprises J solutions, and EVi is an ith electric vehicle;
the EV model is as follows:
a charging capacity when EVi selects the charging solution j is shown in formula (1):
ℳ
i
m
,
j
=
{
η
?
𝒞
?
(
d
i
+
d
i
re
-
d
i
m
)
,
if
⁢
0
<
η
?
𝒞
?
(
d
i
+
d
i
re
-
d
i
m
)
/
ℳ
?
≤
0.9
𝕄
i
m
,
j
,
if
⁢
η
?
𝒞
?
(
d
i
+
d
i
re
-
d
i
m
)
/
ℳ
?
>
0.9
(
1
)
?
indicates text missing or illegible when filed
wherein is the charging capacity of the EVi when the solution j is selected at a road network node m, ηli is a battery energy efficiency coefficient, Cei is the energy consumption per unit mileage of the EVi, di is the remaining mileage of the EVi, dire is the remaining available mileage of the EVi based on user preference, dim is the mileage when the EVi reaches the node m, ri is a rated capacity of a battery of the EVi, is the charging capacity of the EVi obtained by MCS sampling, and dire and satisfies truncated normal distribution;
the cost when EVi selects the charging solution j is the sum of charging cost and time cost, as shown in formula (2):
c
i
(
j
)
=
α
m
j
⁢
ℳ
i
m
,
j
+
INC
i
(
W
q
j
,
t
+
ℳ
i
m
,
j
/
η
dch
⁢
P
r
j
+
s
c
)
/
T
m
(
2
)
wherein ci(j) is the total cost when EVi selects the solution j, αmj is the charging fee per kWh of the solution j;
INCi is the monthly income of the user, Wqj,t is the queuing time of the solution j at time t determined by the M/M/c/∞/∞ queuing theory, dch is the charging efficiency, Prj is the rated power of a charging pile, Sc is the operation time, and Tm is the average monthly working time of the user;
the probability of EVi selecting the solution j is shown in formula (3), and the TMCS charging demand at node m at time t is generated by formula (4);
𝒫
i
(
j
)
=
exp
⁢
(
c
i
(
j
)
)
/
∑
j
J
⁢
exp
⁢
(
c
i
(
j
)
)
(
3
)
P
m
t
=
∑
i
ℳ
i
m
,
j
,
i
∈
χ
t
,
m
tmc
(
4
)
wherein i(j) is the probability that a driver chooses the charging scheme j, Pmt is the hourly EV charging demand of TMCS at node m, and χt,mtmc is the set of the EVs selecting TMCS charging;
the simulation ends when a convergence condition of formula (5) is met, and the spatial-temporal distribution of the EV charging demand is obtained:
max
[
❘
"\[LeftBracketingBar]"
(
∑
π
=
1
Π
P
m
,
π
t
-
∑
π
=
Π
2
⁢
Π
P
m
,
π
t
)
/
Π
❘
"\[RightBracketingBar]"
]
≤
ε
(
5
)
wherein Pm,πt is the hourly EV charging demand at node m after a πth iteration, Π is a predefined number, and ε is the convergence coefficient;
in the step S2, a set M of TMCS operation positions is divided into two disjoint subsets, Mc and Ma, wherein Mc represents a set of charging service nodes, Ma represents a set of arbitrage nodes, and a virtual arc represented by the TMCS start and end points describes the dynamic operation state of the TMCS, so as to obtain the spatial-temporal distribution characteristics of the TMCS;
the virtual arc is divided into a transit arc and a parking arc, the transit arc is composed of two nodes and connecting arcs, the transit arc represents a feasible transit route, the transit arc has a directionality and is divided into a charging transit arc Zc, an arbitrage transit arc Za and a transfer arc Ze according to node types, and the parking arc represents that TMCS stops at a certain node for at least one time period;
the TMCS scheduling model is as follows:
?
ζ
ω
,
mu
t
+
?
ζ
ω
,
nv
t
+
ζ
ω
,
mn
t
=
1
,
∀
ω
∈
Ω
,
t
∈
T
(
6
)
?
ζ
ω
,
mu
t
+
?
ζ
ω
,
mn
t
≥
ζ
ω
,
mn
t
+
1
-
ζ
ω
,
mn
t
,
(
7
)
∀
ω
∈
Ω
,
m
∈
M
c
,
n
∈
M
?
,
t
∈
T
?
ζ
ω
,
nv
t
+
?
ζ
ω
,
mn
t
≥
ζ
ω
,
mn
t
+
1
-
ζ
ω
,
mn
t
,
(
8
)
∀
ω
∈
Ω
,
m
∈
M
c
,
n
∈
M
?
,
t
∈
T
?
+
?
=
?
+
?
,
∀
ω
∈
Ω
,
m
∈
M
c
,
n
∈
M
?
,
t
∈
T
(
9
)
?
+
?
=
?
+
?
,
(
10
)
∀
ω
∈
Ω
,
m
∈
M
c
,
n
∈
M
?
,
t
∈
T
?
+
?
=
?
,
(
11
)
∀
ω
∈
Ω
,
m
∈
M
c
,
n
∈
M
?
?
+
?
=
?
,
∀
ω
∈
Ω
,
m
∈
M
c
,
n
∈
M
?
(
12
)
ζ
ω
,
mu
t
+
ζ
ω
,
mu
t
+
1
≤
1
,
∀
(
m
,
u
)
∈
Z
c
,
m
≠
u
,
t
∈
T
(
13
)
ζ
ω
,
mu
t
+
ζ
ω
,
mu
t
+
1
≤
1
,
∀
(
n
,
v
)
∈
Z
a
,
n
≠
v
,
t
∈
T
(
14
)
ζ
ω
,
mu
t
+
ζ
ω
,
mu
t
+
1
≤
1
,
∀
(
m
,
n
)
∈
Z
e
,
m
≠
n
,
t
∈
T
(
15
)
?
indicates text missing or illegible when filed
wherein ω is the number of TMCS, Ω is the set of TMCS, T is the set of hourly periods t, m and u are road network charging service nodes, n and v are energy arbitrage nodes, ζω,mut, ζω,nvt, ζω,mnt, ζω,mmt, ζω,nnt, ζω,umt, ζω,nmt, ζω,vnt, ζω,mmt+1, ζω,nnt+1, ζω,mut+1, ζω,mmt+1, ζω,nvt+1, ζω,nvte+1, ζω,nmt+1, ζω,nmte+1, ζω,nnte, ζω,vnte−1, ζω,mnte−1, ζω,umt+1, ζω,vnt+1, and ζω,nmt+1 are binary variables, and represent whether the TMCS numbered ω is on the corresponding transit arc at the corresponding time;
when the TMCS ω is on the transit arc (m, u) at the time t, ζω,nmt=1, and when the TMCS ω is not on the transit arc (m, u), ζω,mut=0, and the same is true for other binary variables;
te is the time for ending the work of the TMCS;
and
Z+c represents a forward-direction charging arc, Z−e represents a reverse-direction charging arc, Z+a, represents a forward-direction arbitrage arc, Z−a represents a reverse-direction arbitrage arc, Z+e represents a forward-direction transfer arc, and Z−e represents a reverse-direction transfer arc.

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Day-ahead coordinated optimization scheduling method for trunk mobile charging station

Life of this patent

Day-ahead coordinated optimization scheduling method for trunk mobile charging station

Life of this patent

Abstract

Claims3